'''simple docstring'''




import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       int:
    __lowerCamelCase            =    old_name

    if "patch_embed" in old_name:
        __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    old_name.split('''.'''							)

        if layer == "0":
            __lowerCamelCase            =    old_name.replace('''0'''							,    '''convolution1'''							)
        elif layer == "1":
            __lowerCamelCase            =    old_name.replace('''1'''							,    '''batchnorm_before'''							)
        elif layer == "3":
            __lowerCamelCase            =    old_name.replace('''3'''							,    '''convolution2'''							)
        else:
            __lowerCamelCase            =    old_name.replace('''4'''							,    '''batchnorm_after'''							)

    if "network" in old_name and re.search(r'''\d\.\d'''							,    UpperCamelCase__							):
        __lowerCamelCase            =    r'''\b\d{2}\b'''
        if bool(re.search(UpperCamelCase__							,    UpperCamelCase__							)							):
            __lowerCamelCase            =    re.search(r'''\d\.\d\d.'''							,    UpperCamelCase__							).group()
        else:
            __lowerCamelCase            =    re.search(r'''\d\.\d.'''							,    UpperCamelCase__							).group()
        if int(match[0]							) < 6:
            __lowerCamelCase            =    old_name.replace(UpperCamelCase__							,    ''''''							)
            __lowerCamelCase            =    trimmed_name.replace('''network'''							,    match[0] + '''.meta4D_layers.blocks.''' + match[2:-1]							)
            __lowerCamelCase            =    '''intermediate_stages.''' + trimmed_name
        else:
            __lowerCamelCase            =    old_name.replace(UpperCamelCase__							,    ''''''							)
            if int(match[2]							) < num_meta4D_last_stage:
                __lowerCamelCase            =    trimmed_name.replace('''network'''							,    '''meta4D_layers.blocks.''' + match[2]							)
            else:
                __lowerCamelCase            =    str(int(match[2]							) - num_meta4D_last_stage							)
                __lowerCamelCase            =    trimmed_name.replace('''network'''							,    '''meta3D_layers.blocks.''' + layer_index							)
                if "norm1" in old_name:
                    __lowerCamelCase            =    trimmed_name.replace('''norm1'''							,    '''layernorm1'''							)
                elif "norm2" in old_name:
                    __lowerCamelCase            =    trimmed_name.replace('''norm2'''							,    '''layernorm2'''							)
                elif "fc1" in old_name:
                    __lowerCamelCase            =    trimmed_name.replace('''fc1'''							,    '''linear_in'''							)
                elif "fc2" in old_name:
                    __lowerCamelCase            =    trimmed_name.replace('''fc2'''							,    '''linear_out'''							)

            __lowerCamelCase            =    '''last_stage.''' + trimmed_name

    elif "network" in old_name and re.search(r'''.\d.'''							,    UpperCamelCase__							):
        __lowerCamelCase            =    old_name.replace('''network'''							,    '''intermediate_stages'''							)

    if "fc" in new_name:
        __lowerCamelCase            =    new_name.replace('''fc'''							,    '''convolution'''							)
    elif ("norm1" in new_name) and ("layernorm1" not in new_name):
        __lowerCamelCase            =    new_name.replace('''norm1'''							,    '''batchnorm_before'''							)
    elif ("norm2" in new_name) and ("layernorm2" not in new_name):
        __lowerCamelCase            =    new_name.replace('''norm2'''							,    '''batchnorm_after'''							)
    if "proj" in new_name:
        __lowerCamelCase            =    new_name.replace('''proj'''							,    '''projection'''							)
    if "dist_head" in new_name:
        __lowerCamelCase            =    new_name.replace('''dist_head'''							,    '''distillation_classifier'''							)
    elif "head" in new_name:
        __lowerCamelCase            =    new_name.replace('''head'''							,    '''classifier'''							)
    elif "patch_embed" in new_name:
        __lowerCamelCase            =    '''efficientformer.''' + new_name
    elif new_name == "norm.weight" or new_name == "norm.bias":
        __lowerCamelCase            =    new_name.replace('''norm'''							,    '''layernorm'''							)
        __lowerCamelCase            =    '''efficientformer.''' + new_name
    else:
        __lowerCamelCase            =    '''efficientformer.encoder.''' + new_name

    return new_name






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       Union[str, Any]:
    for key in checkpoint.copy().keys():
        __lowerCamelCase            =    checkpoint.pop(UpperCamelCase__							)
        __lowerCamelCase            =    val

    return checkpoint






def __lowerCAmelCase					(				)      ->       str:
    __lowerCamelCase            =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __lowerCamelCase            =    Image.open(requests.get(UpperCamelCase__							,    stream=UpperCamelCase__							).raw							)

    return image






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       int:
    __lowerCamelCase            =    torch.load(UpperCamelCase__							,    map_location='''cpu'''							)['''model''']
    __lowerCamelCase            =    EfficientFormerConfig.from_json_file(UpperCamelCase__							)
    __lowerCamelCase            =    EfficientFormerForImageClassificationWithTeacher(UpperCamelCase__							)
    __lowerCamelCase            =    '''_'''.join(checkpoint_path.split('''/'''							)[-1].split('''.'''							)[0].split('''_'''							)[:-1]							)

    __lowerCamelCase            =    config.depths[-1] - config.num_metaad_blocks + 1
    __lowerCamelCase            =    convert_torch_checkpoint(UpperCamelCase__							,    UpperCamelCase__							)

    model.load_state_dict(UpperCamelCase__							)
    model.eval()

    __lowerCamelCase            =    {
        '''bilinear''': PILImageResampling.BILINEAR,
        '''bicubic''': PILImageResampling.BICUBIC,
        '''nearest''': PILImageResampling.NEAREST,
    }

    # prepare image
    __lowerCamelCase            =    prepare_img()
    __lowerCamelCase            =    2_56
    __lowerCamelCase            =    2_24
    __lowerCamelCase            =    EfficientFormerImageProcessor(
        size={'''shortest_edge''': image_size}							,    crop_size={'''height''': crop_size, '''width''': crop_size}							,    resample=pillow_resamplings['''bicubic''']							,    )
    __lowerCamelCase            =    processor(images=UpperCamelCase__							,    return_tensors='''pt'''							).pixel_values

    # original processing pipeline
    __lowerCamelCase            =    Compose(
        [
            Resize(UpperCamelCase__							,    interpolation=pillow_resamplings['''bicubic''']							),
            CenterCrop(UpperCamelCase__							),
            ToTensor(),
            Normalize(UpperCamelCase__							,    UpperCamelCase__							),
        ]							)
    __lowerCamelCase            =    image_transforms(UpperCamelCase__							).unsqueeze(0							)

    assert torch.allclose(UpperCamelCase__							,    UpperCamelCase__							)

    __lowerCamelCase            =    model(UpperCamelCase__							)
    __lowerCamelCase            =    outputs.logits

    __lowerCamelCase            =    (1, 10_00)

    if "l1" in model_name:
        __lowerCamelCase            =    torch.Tensor(
            [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]							)
        assert torch.allclose(logits[0, :10]							,    UpperCamelCase__							,    atol=1E-3							)
        assert logits.shape == expected_shape
    elif "l3" in model_name:
        __lowerCamelCase            =    torch.Tensor(
            [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]							)
        assert torch.allclose(logits[0, :10]							,    UpperCamelCase__							,    atol=1E-3							)
        assert logits.shape == expected_shape
    elif "l7" in model_name:
        __lowerCamelCase            =    torch.Tensor(
            [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]							)
        assert logits.shape == expected_shape
    else:
        raise ValueError(
            f"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""							)

    # Save Checkpoints
    Path(UpperCamelCase__							).mkdir(exist_ok=UpperCamelCase__							)
    model.save_pretrained(UpperCamelCase__							)
    print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""							)
    processor.save_pretrained(UpperCamelCase__							)
    print(f"""Processor successfuly saved at {pytorch_dump_path}"""							)

    if push_to_hub:
        print('''Pushing model to the hub...'''							)

        model.push_to_hub(
            repo_id=f"""Bearnardd/{pytorch_dump_path}"""							,    commit_message='''Add model'''							,    use_temp_dir=UpperCamelCase__							,    )
        processor.push_to_hub(
            repo_id=f"""Bearnardd/{pytorch_dump_path}"""							,    commit_message='''Add image processor'''							,    use_temp_dir=UpperCamelCase__							,    )


if __name__ == "__main__":
  __UpperCAmelCase					=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--pytorch_model_path",
      default=None,
      type=str,
      required=True,
      help="Path to EfficientFormer pytorch checkpoint.",
  )
  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help="The json file for EfficientFormer model config.",
  )
  parser.add_argument(
      "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )

  parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
  parser.add_argument(
      "--no-push_to_hub",
      dest="push_to_hub",
      action="store_false",
      help="Do not push model and image processor to the hub",
  )
  parser.set_defaults(push_to_hub=True)

  __UpperCAmelCase					=parser.parse_args()
  convert_efficientformer_checkpoint(
      checkpoint_path=args.pytorch_model_path,
      efficientformer_config_file=args.config_file,
      pytorch_dump_path=args.pytorch_dump_path,
      push_to_hub=args.push_to_hub,
  )

'''simple docstring'''




import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch




@require_torch
class        a__ :






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         return self.get_dummy_input()






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         if self.block_type == "down":
             return (4, 32, 16, 16)
         elif self.block_type == "mid":
             return (4, 32, 32, 32)
         elif self.block_type == "up":
             return (4, 32, 64, 64)

         raise ValueError(f"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						List[Any]=True    ,							a						:						Dict=False    ,							a						:						Optional[Any]=False    ,							a						:						List[Any]=False    ,							):





         """simple docstring"""



         __lowerCamelCase            =    4
         __lowerCamelCase            =    32
         __lowerCamelCase            =    (32, 32)

         __lowerCamelCase            =    torch.manual_seed(0       )
         __lowerCamelCase            =    torch.device(a       )
         __lowerCamelCase            =    (batch_size, num_channels) + sizes
         __lowerCamelCase            =    randn_tensor(a    ,							generator=a    ,							device=a       )
         __lowerCamelCase            =    {'''hidden_states''': hidden_states}

         if include_temb:
             __lowerCamelCase            =    1_28
             __lowerCamelCase            =    randn_tensor((batch_size, temb_channels)    ,							generator=a    ,							device=a       )

         if include_res_hidden_states_tuple:
             __lowerCamelCase            =    torch.manual_seed(1       )
             __lowerCamelCase            =    (randn_tensor(a    ,							generator=a    ,							device=a       ),)

         if include_encoder_hidden_states:
             __lowerCamelCase            =    floats_tensor((batch_size, 32, 32)       ).to(a       )

         if include_skip_sample:
             __lowerCamelCase            =    randn_tensor(((batch_size, 3) + sizes)    ,							generator=a    ,							device=a       )

         return dummy_input






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         __lowerCamelCase            =    {
             '''in_channels''': 32,
             '''out_channels''': 32,
             '''temb_channels''': 1_28,
         }
         if self.block_type == "up":
             __lowerCamelCase            =    32

         if self.block_type == "mid":
             init_dict.pop('''out_channels'''       )

         __lowerCamelCase            =    self.dummy_input
         return init_dict, inputs_dict






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						List[str]       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase            =    self.prepare_init_args_and_inputs_for_common()
         __lowerCamelCase            =    self.block_class(**a       )
         unet_block.to(a       )
         unet_block.eval()

         with torch.no_grad():
             __lowerCamelCase            =    unet_block(**a       )

         if isinstance(a    ,							a       ):
             __lowerCamelCase            =    output[0]

         self.assertEqual(output.shape    ,							self.output_shape       )

         __lowerCamelCase            =    output[0, -1, -3:, -3:]
         __lowerCamelCase            =    torch.tensor(a       ).to(a       )
         assert torch_all_close(output_slice.flatten()    ,							a    ,							atol=5e-3       )






     @unittest.skipIf(torch_device == '''mps'''    ,							'''Training is not supported in mps'''       )
     def   SCREAMING_SNAKE_CASE__  (					self						:						Any       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase            =    self.prepare_init_args_and_inputs_for_common()
         __lowerCamelCase            =    self.block_class(**a       )
         model.to(a       )
         model.train()
         __lowerCamelCase            =    model(**a       )

         if isinstance(a    ,							a       ):
             __lowerCamelCase            =    output[0]

         __lowerCamelCase            =    torch.device(a       )
         __lowerCamelCase            =    randn_tensor(output.shape    ,							device=a       )
         __lowerCamelCase            =    torch.nn.functional.mse_loss(a    ,							a       )
         loss.backward()
